Associations between day and night-shifts, work-related musculoskeletal symptoms and absenteeism in the manufacturing industry.

Objectives. Associations between shift-work, musculoskeletal symptoms and absenteeism are poorly investigated in the manufacturing industry. This study aimed to investigate associations between working schedule, musculoskeletal symptoms and days of absenteeism among pulp and paper industry workers. Methods. Musculoskeletal symptoms of 904 workers were assessed through the Nordic Musculoskeletal Questionnaire. χ2 tests assessed associations between being a day-worker or shift-worker, the prevalence of musculoskeletal symptoms and days of absenteeism. Results. A significant association was found between working schedule and symptoms in the lower back in the last 12 months, with shift-workers presenting higher prevalence than day-workers (p = 0.022). Significant associations were also found between days of absenteeism and symptoms in the shoulders (p = 0.002), which mostly led to absenteeism of 100-365 days; elbows (p < 0.001), wrists/hands (p = 0.045) and ankles/feet (p = 0.042), which produced absenteeism mostly of 25-99 days; and dorsal region (p = 0.001), which mainly led to absenteeism of 10-24 days. No associations were found between working schedule and days of absenteeism (p = 0.265). Conclusion. Shift-work is associated with increased prevalence of lower back symptoms, but seems not to influence days of absenteeism. Shoulders seem to be the region leading to higher days of absenteeism, followed by elbows, wrists/hands, ankles/feet and the dorsal region.

Impact of digital technology innovation on carbon intensity: evidence from China's manufacturing A-share listed enterprises.

Current studies do not provide a consensus on whether digital technology innovation can reduce enterprise carbon intensity despite the rise of the digital economy. This paper examines the role and influence pathway of digital technology innovation on enterprise carbon intensity using data from A-share listed enterprises in China's manufacturing industry from 2012 to 2021. The findings indicate that (1) digital technology innovation has been found to significantly reduce enterprise carbon intensity, as confirmed by numerous robustness and endogeneity tests. However, its inhibitory effect on carbon intensity shows a marginal decreasing trend. (2) In the heterogeneity analysis, it was found that digital technology innovation significantly reduces the carbon intensity of consuming coal, coke, kerosene, and diesel. From various perspectives, including enterprise, industry, and external environment, there are significant differences in the carbon reduction effects of digital technology innovation. (3) The analysis of impact paths reveals that digital technology innovation can affect enterprise carbon intensity through three paths: improving productivity, enhancing green innovation efficiency, and adjusting energy consumption. (4) Upon further analysis, it was discovered that the spillover effect of digital technology innovation is more pronounced in the industry cohort of enterprises. Additionally, digital technology innovation plays a positive role in enhancing enterprise ESG performance. The paper's findings offer empirical evidence and decision-making references for the government to develop reasonable policies for reducing carbon emissions, promoting green and low-carbon enterprise transformation, and actively and steadily achieving the goal of carbon peaking and carbon neutrality.

Analysis of the spatiotemporal evolution and influencing factors of green development level in the manufacturing industry.

The manufacturing sector is the main battlefield of energy saving and carbon reduction in China, and vigorously promoting energy saving and carbon reduction in manufacturing and enhancing the green development level are the key links to support China's realization of the dual-carbon goal. The article adopts the SBM-GML model to measure the level of green development of the manufacturing industry in China. Based on this, it analyzes the spatio-temporal characteristics and the evolution law of the level of green development of the manufacturing industry by using the Dagum Gini Coefficient and Kernel Density Estimation. Using a spatial econometric model to explore the influencing factors of the level of green development of the manufacturing industry. The study finds that the green development level of the manufacturing industry has achieved remarkable results in recent years, but there are differences in the development level of each region. The regional differences in the level of green development of the manufacturing industry are significant. The optimization of manufacturing structure is a key factor influencing the level of green development of the manufacturing industry, and there is a positive spatial spillover effect of manufacturing structure optimization. However, The green development of the manufacturing industry shows a negative spatial spillover effect. The article proposes optimization paths based on the requirements of dual-carbon targets and regional characteristics, which is an important inspiration and reference for the green development level of the manufacturing industry in the world.

Modification of the Revised National Institute for Occupational Safety and Health (NIOSH) Lifting Equation to Determine the Individual Manual Lifting Risk in Malaysia's Manufacturing Industry.

INTRODUCTION: The National Institute for Occupational Safety and Health (NIOSH) established the Revised NIOSH Lifting Equation (RNLE) for manual lifting risk assessment. The objectives of this study were to determine the characteristics of physical factors using the RNLE and to explore additional factors to RNLE by modifying it to an Individual Lifting Equation (ILE). METHODS: This cross-sectional study was conducted in the manufacturing industry of three states in Malaysia among manual lifting workers. A questionnaire was administered, which comprised the sociodemographic characteristics and Nordic Musculoskeletal Questionnaire (NMQ) assessing low back pain (LBP). The RNLE dataset includes a load constant and six manual lifting variables collected from observational ergonomic risk assessment. The RNLE was modified to ILE by incorporating age, gender, and BMI. The equations' Lifting Index (LI) computed provides an overall manual lifting risk estimate. RESULTS: There were 165 participants, with a mean age of 28 years, and 108 (65.5%) were male. Most participants had a BMI within the normal range (60 (36.4%)) or were classified as overweight (54 (32.7%)). The lifting horizontal location showed the highest risk estimates, with the lowest mean multiplier value of 0.55. In contrast, age and BMI had the lowest risk estimates, with mean multiplier values of 0.99 and 0.98, respectively. Among the participants, LI values of one or less, indicating very low risk, were observed in 58 (35.1%) for RNLE and 39 (23.6%) for ILE. Additionally, RNLE and ILE showed figures of 11 (6.7%) and 20 (12.1%), respectively, signifying a very high risk of LI exceeding three. CONCLUSION: Studying the lifting factors and equation multipliers from RNLE is critical for evaluating the risk estimates of manual lifting. Exploring the ILE based on individual characteristics is appropriate to support the ergonomic program. Further study is needed to validate the ILE as an accurate screening tool for determining LBP risk estimates.

Emission factors and source profiles of volatile organic compounds from the automobile manufacturing industry.

Controlling volatile organic compounds (VOCs) emitted from the automobile manufacturing industry requires establishing VOCs emission factors (EFs) and source profiles refinedly. In this study, 41 samples involved 32 VOCs discharge links were collected from three factories. The EFs and VOCs source profiles were estimated by the material balance method and weighted average method, respectively. The ozone formation potential (OFP) of the 110 VOCs species were calculated by the maximum incremental reactivity (MIR). According to estimations, the ranges of EFs were 0.23-1.66 kg VOCs/SUV car and 2.14-14.86 g VOCs/m2 painted area. EFs of six materials were firstly estimated, which are electrophoretic primer (152.31 ± 97.39 g VOCs/SUV car, 0.97 ± 0.38 g VOCs/m2 painted area), sealant (48.39 ± 26.20 g VOCs/SUV car, 0.46 ± 0.25 g VOCs/m2 painted area), floating coat (87.40 ± 75.63 g VOCs/SUV car, 0.86 ± 0.74 g VOCs/m2 painted area), colored paint (127.24 ± 168.24 g VOCs/SUV car, 1.25 ± 1.66 g VOCs/m2 painted area), varnish (205.46 ± 218.14 g VOCs/SUV car, 2.01 ± 2.15 g VOCs/m2 painted area), and cleaning solvent (328.54 ± 404.94 g VOCs/SUV car, 3.23 ± 3.98 g VOCs/m2 painted area). OVOCs (37.40-51.60 %) and aromatics (36.40-37.00 %) were the dominant components. n-Butyl acetate, 1,2,4-trimethylbenzene, undecane, n-hexanal, acetone, 1,2,3-trimethylbenzene, 1,3,5 -trimethylbenzene, m/p/o-xylene, 3-ethylbenzene, and 4-ethylbenzene were the major VOCs species, accounting for 68 % of total VOCs in the automobile manufacturing industry. Considering the OFP values of species, 1,2,4-trimethylbenzene, 1,3,5-trimethylbenzene, 1,2,3-trimethylbenzene, m/p-xylene, acetaldehyde, methyl ethyl ketone are the key active species that should be prioritized for control.

[Application and comparison of three occupational health risk assessment methods in an automobile manufacturing industry].

Objective: Three occupational health risk assessment methods were used to assess the occupational health risk of noise exposed posts in an automobile manufacturing enterprise. According to the results, the selection of risk assessment methods and risk management of such occupational noise enterprises were provided. Methods: Form April to November 2021, The occupational health field survey was carried out in an automobile manufacturing industry in Tianjin. The occupational health MES risk assessment method, occupational health risk index risk assessment method and Australian occupational hazard risk assessment method were used to evaluate the occupational health risk of noise-exposed posts in this enterprise, and the evaluation results of different methods were analyzed and compared. Results: The average value of L(Aeq, 8 h) in the four workshops of automobile manufacturing industry was 82.95 dB (A) , and the noise detection exceeding rate was 22.41% (26/116) . The LAeq, 8h and exceeding rate noise of welding workshop were higher than those of other workshops (χ(2)=23.56, 32.94, P<0.01) . The three occupational health risk assessment methods have the same risk assessment results for the four major workshops. The assembly and painting workshops are level 4 risk (possible risk) , and the stamping and welding workshops are level 3 risk (significant risk) . Conclusion: Occupational noise has certain potential hazards to workers in automobile manufacturing enterprises. Therefore, in the future work, corresponding organizational management measures should be taken to improve the working environment and reduce the actual exposure level of workers in order to protect the health of occupational workers.

Can digital finance development drive green transformation in manufacturing? Evidence from China.

Digital finance is a product of emerging technology-enabled innovation in financial services and has a critical impact on the green transformation of the manufacturing industry. We propose a new efficiency measurement model based on the slacks-based measure (SBM) to measure the efficiency of green transformation on regional manufacturing. Chinese interprovincial data from 2010 to 2019 were obtained for the study. In addition, we estimated the effect of digital finance on green transformation of manufacturing using a benchmark panel model. Finally, considering the regional heterogeneity and spatial effects of green transformation efficiency in the manufacturing industry, we constructed a spatial Durbin model based on an economic-geographic nested spatial weight matrix to analyze the spatial influence of digital finance on green transformation in the manufacturing industry. The results show that the green transformation of the manufacturing industry has significant positive spatial spillover effects owing to the existence of competition, demonstration, and economic correlation effects among regions.

Content and face validity of Workplace COVID-19 Knowledge & Stigma Scale (WoCKSS).

BACKGROUND: The COVID-19 pandemic has led to fear, rumours, and stigma, particularly against those infected with the virus. In Malaysia, the manufacturing industry is particularly vulnerable to COVID-19 clusters, making it critical to assess stigma attitudes among workers. To address this issue, The Workplace COVID-19 Knowledge & Stigma Scale (WoCKSS) was developed specifically for use in the manufacturing industry which served as the sample population for testing this scale. It was developed in the Malay language to ensure alignment with the local context. This study examines the content and face validity of WoCKSS, which can help assess the level of knowledge and stigma associated with COVID-19 among workers. METHODS: The WoCKSS was developed with 20 and 31 items for knowledge and stigma domains, respectively, based on an extensive review of COVID-19 literature. Content validation was conducted by four experts using a content validation form to assess the relevancy of each item to the intended construct. Content Validity Index (CVI) was calculated to measure the agreement between the experts on the relevance of each item to the intended construct. Face validation was then conducted by randomly selecting 10 respondents from the manufacturing industry, who rated the clarity and comprehension of each item using a face validation form. The Item Face Validity Index (I-FVI) was calculated to determine the clarity and comprehension of each question, and only items with an I-FVI ≥ 0.83 were retained. RESULTS: The WoCKSS achieved excellent content validity in both knowledge and stigma domains. Only 19 items from the knowledge domain and 24 items from the stigma domain were retained after CVI analysis. All retained items received a CVI score of 1.00, indicating perfect agreement among the experts. FVI analysis resulted in 17 items for the knowledge domain and 22 items for the stigma domain. The knowledge domain achieved a high level of agreement among respondents, with a mean I-FVI of 0.91 and a S-FVI/UA of 0.89. The stigma domain also showed high agreement, with a mean I-FVI of 0.99 and a S-FVI/UA of 0.86. CONCLUSION: In conclusion, the WoCKSS demonstrated high content and face validity. However, further testing on a larger sample size is required to establish its construct validity and reliability.

Impacts of ergonomic risk factors on the well-being and innovation capability of employees in the manufacturing industry.

Objectives. Although various studies have examined the relationship between ergonomic factors and employee well-being, the impacts of ergonomic factors on workers' capability for innovation have not yet been studied in the manufacturing industry. This study advances understanding of the relationship between ergonomic risk factors and employees' capability to innovate and to feel good at work in the manufacturing sector. Methods. The analysis uses the structural equation modeling technique based on cross-sectional data collected from 200 experienced workers in manufacturing industries using self-administered close-ended questionnaires. Results. Findings from this research show that the main ergonomic factors influencing the well-being and innovation capability of employees in the manufacturing industry are neutral awkward posture, psychological risk factors and effective utilization of information and communications technology infrastructures. Thus, ergonomic factors are significantly correlated to the innovation capability of employees. Conclusion. As there have been no studies addressing the integration of ergonomic risk factors and the capability for innovation of employees in the manufacturing industry, this study provides a unique contribution to the body of knowledge. Further research is also required to develop an in-depth understanding of the relationship among components of each ergonomic risk factor, and the well-being and innovation capability of employees.

Vision-based omnidirectional indoor robots for autonomous navigation and localization in manufacturing industry.

In this paper, we present a new generation of omnidirectional automated guided vehicles (omniagv) used for transporting materials within a manufacturing factory with the ability to navigate autonomously and intelligently by interacting with the environment, including people and other entities. This robot has to be integrated into the operating environment without significant changes to the current facilities or heavy redefinitions of the logistics processes already running. For this purpose, different vision-based systems and advanced methods in mobile and cognitive robotics are developed and integrated. In this context, vision and perception are key factors. Different developed modules are in charge of supporting the robot during its navigation in the environment. Specifically, the localization module provides information about the robot pose by using visual odometry and wheel odometry systems. The obstacle avoidance module can detect obstacles and recognize some object classes for adaptive navigation. Finally, the tag detection module aids the robot during the picking phase of carts and provides information for global localization. The smart integration of vision and perception is paramount for effectively using the robot in the industrial context. Extensive qualitative and quantitative results prove the capability and effectiveness of the proposed AGV to navigate in the considered industrial environment.

Emission factors and source profiles of volatile organic compounds in container manufacturing industry.

The container manufacturing industry is the key contributor of industrial volatile organic compounds (VOCs). Emission factors (EFs) and source profiles of container manufacturing industry were comprehensively investigated basing on multiple VOCs discharge links. 17 samples were collected from a typical container manufacturing enterprise based on field measurements. The material balance method and weighted average method were applied to estimate EFs and establish VOCs source profiles. It is found that diluent use (DU) was the largest contributor (39.96 %), followed by intermediate painting spaying (IMPS), primer painting (PP), chassis painting (CP), exterior paint spaying (EPS), and interior paint spaying (IPS). EF of the container manufacturing industry (2.90 kg VOCs/ Twenty-foot Equivalent Units, TEU) was firstly estimated. EFs of six processes were further estimated. The EFs of DU, IMPS, PP, CP, EPS, and IPS were 1.22, 0.74, 0.42, 0.33, 0.20, and 0.00045 kg VOCs/TEU, respectively. EFs of six materials were further estimated. The EF of the diluent was largest (382.74 kg VOCs/t material), followed by water-based epoxy intermediate paint (132.09 kg VOCs/t material), organic-based epoxy zinc-rich priming paint (91.31 kg VOCs/t material). EFs of other paints ranged from 0.0047 to 43.01 kg VOCs/t material. These results suggest that the replacement of lower- VOCs- contained diluent and effective control from diluent consumption are dramatically conducive to VOCs reduction. Source profiles were established at the industry and individual process levels. Aromatics (77.05-98.38 %) were dominant components in all processes, followed by alkane and OVOCs. m/p-Xylene, o-xylene, and ethylbenzene were the key active species that should be prioritized for control. Overall, EFs and source profiles of the container manufacturing industry were firstly proposed, conducing to the systematic formulation of VOCs control strategies.

A holistic methodology for mitigating awkward postural risks: Evidence from South Indian small-scale industries.

BACKGROUND: Small-scale industries (SSI) are the global economy's backbone since most industrial workers are connected. Most of these workers are contractual and temporary without appropriate training. Also, the SSI does not have a standard workplace with an appropriate layout and infrastructure, as they manage with minimum resources. Therefore, the work hazards, i.e., musculoskeletal disorders and fatigue, often go unnoticed as holistic postural risk methodology is still scarce for identifying the awkward postures in SSI. OBJECTIVE: The present study proposes a novel holistic methodology to track and mitigate awkward postural risks in human-physical activities in SSI. To determine the effectiveness of the proposed methodology, a case study is presented in the South Indian Pump industry, wherein a critical workstation with a complex ergonomic work environment is employed. METHODS: An ergonomic evaluation was conducted empirically and numerically in the workplaces using Digital Human Models. In numerical evaluation, three virtual workspaces have been created to redesign the identified crucial workstation, focusing on ergonomics and workflow. RESULTS: The results obtained from the case study are encouraging for to use of the novel methodology in SSI. The case study reports that the proposed design significantly reduced the REBA score and WISHA lifting index by 6 and 1.20, respectively, without significant investment. CONCLUSION: The proposed methodology could encourage research to identify awkward posture in SSI.

Green production and green technology for sustainability: The mediating role of waste reduction and energy use.

The study investigates the relationship between green production, green technology, waste reduction, energy use, and sustainability. A Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was used for analysis. The data was collected from a sample of companies in the textile industry. The results suggest that green production and technology positively and significantly affect waste reduction and energy use, which mediates the positive relationship between these two factors and sustainability. This study concludes that green production and technology are critical drivers of sustainability and emphasizes the need to prioritize waste reduction and energy use in sustainable manufacturing practices. The study has practical and managerial implications in all production or manufacturing industries and provides a guideline for managers and policymakers to ensure sustainability.

Has the consistency evaluation policy of generic drugs promoted the innovation quality of Chinese pharmaceutical manufacturing industry? An empirical study based on the difference-in-differences model.

Introduction: In March 2016, the Chinese government officially launched a nationwide consistency evaluation of the quality and efficacy of generic drugs. Methods: This paper conducted an empirical study using the Difference-in-Differences method to explore the effect of this policy on the innovation quality of China's pharmaceutical manufacturing industry and further analyzed the underlying mechanism of action. Results: The results of the study show that the generic consistency evaluation policy has a significant promotion effect on the innovation quality of China's pharmaceutical manufacturing industry, and the promotion effect is the largest for non-state-owned enterprises and enterprises in the central region; in addition, the intensity of R&D capital investment and R&D personnel investment which play a mediating role. Discussion: Therefore, we should fully recognize the positive effect of generic drug consistency evaluation policy on improving the innovation quality of the pharmaceutical manufacturing industry and pay attention to the necessity of regional coordination and unification in policy implementation and the formulation of supporting policy tools. This study provides empirical evidence for the implementation effect of the generic drug consistency evaluation policy, which can provide an essential reference for the further improvement of the procedure and the R&D decision-making of pharmaceutical enterprises.

Explainability as the key ingredient for AI adoption in Industry 5.0 settings.

Explainable Artificial Intelligence (XAI) has gained significant attention as a means to address the transparency and interpretability challenges posed by black box AI models. In the context of the manufacturing industry, where complex problems and decision-making processes are widespread, the XMANAI platform emerges as a solution to enable transparent and trustworthy collaboration between humans and machines. By leveraging advancements in XAI and catering the prompt collaboration between data scientists and domain experts, the platform enables the construction of interpretable AI models that offer high transparency without compromising performance. This paper introduces the approach to building the XMANAI platform and highlights its potential to resolve the "transparency paradox" of AI. The platform not only addresses technical challenges related to transparency but also caters to the specific needs of the manufacturing industry, including lifecycle management, security, and trusted sharing of AI assets. The paper provides an overview of the XMANAI platform main functionalities, addressing the challenges faced during the development and presenting the evaluation framework to measure the performance of the delivered XAI solutions. It also demonstrates the benefits of the XMANAI approach in achieving transparency in manufacturing decision-making, fostering trust and collaboration between humans and machines, improving operational efficiency, and optimizing business value.

Analysis of the fire risks and mitigation approaches in the apparel manufacturing industry: Implications toward operational safety and sustainability.

Fire risks pose a substantial threat to the apparel manufacturing industry since they can lead to immense property damage, potential loss of life, disruption of business operations, and reputational damage. In an emerging economy like Bangladesh, fire-related hazards are crucial due to the numerous deadly industrial fire incidents in recent years. This research, thereby, proposes an integrated multi-criteria decision-making (MCDM) framework to identify and mitigate fire risk hazards in the apparel manufacturing industry. Initially, the study identified 30 significant fire risk factors from the literature review. Then, after expert validation, an integrated Best Worst Method (BWM) and Weighted Sum Model (WSM) framework was utilized to prioritize the fire risk factors. Twenty-three mitigation actions were proposed afterward for the top-ranked risk factors based on National Fire Protection Association (NFPA) codes. An Interpretive Structural Modeling (ISM) with a Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis was later used to explore the interrelationships and dependencies among the mitigation actions. The ranking obtained from the BWM-WSM assessment revealed 'combustible storage unseparated by fire-rated construction,' 'non-standard inspection, testing, and maintenance', and 'inadequate means of egress for the occupant load' as the three most critical fire risk factors. The ISM-MICMAC analysis revealed 'fire-rated construction' and 'standardized detection and protection' as the most-driving mitigation actions. The study outcomes are expected to aid the managers and policymakers in emerging economies in formulating sustainable fire risk management strategies for the apparel industry and thus improve the operational safety and resilience of the sector.

Environmental taxes, enterprise innovation, and environmental total factor productivity-effect test based on Porter's hypothesis.

Under the increasingly severe environmental constraints, improving environmental total factor productivity (ETFP) is the fundamental way for the sustainable development of heavily polluting enterprises. Based on 3463 panel data of A-share listed companies in China from 2011 to 2019, this paper employs Porter's hypothesis (PH) framework to explore the impact of environmental tax (EN_T) on enterprise innovation and environmental total factor productivity for the heavily polluting manufacturing industry using the propensity score matching (PSM) method. The empirical results show the following. (i) Environmental taxes positively affect enterprise innovation (EI) and environmental total factor productivity (ETFP). (ii) Mechanism analysis verifies a partial mediating effect for EI between EN_T and ETFP. (iii) Regional heterogeneity analysis illustrates the differences in the impact of environmental taxes on innovation quality. (iv) Individual heterogeneity analysis shows that the "strong Porter hypothesis" is only valid for large-scale enterprises. The results are of great importance for both government and enterprises to improve the EN_T system and optimize the allocation of resources in realistic practice.

Analysis of coupled coordination and spatial interaction effects between manufacturing and logistics industries in the Yellow River Basin of China.

The coupled and coordinated development of the manufacturing and logistics industries has become an inevitable choice for achieving high-quality development in both sectors. In this study, we focused on nine provinces located in the Yellow River Basin and analyzed panel data from 2010 to 2021. Our analysis, based on the super-efficient SBM-undesirable model, revealed that the coupling and coordination efficiency between the two industries in the region is moderate, with significant regional disparities. Additionally, using the Global Moran's I and the local Moran's I, we tested the spatial autocorrelation of the two industries and analyzed their spatial interaction effect using SDM. The study reveals that the manufacturing and logistics industries in the Yellow River Basin exhibit moderate coupling and coordination efficiency with significant regional variations. We found that the logistics industry plays a more supportive role in the manufacturing industry, particularly in Henan and Shandong provinces. Spatial spillover effects in terms of informationization, openness to the outside world, and energy consumption are more significant, while infrastructure investment does not exhibit significant spatial interaction effects. Based on our findings, we propose relevant development strategies for the two industries.

Analyzing dynamic coupling and coordination of modern service and advanced manufacturing industries in Guangdong-Hong Kong-Macao Greater Bay Area.

Integrating the modern service industry with the advanced manufacturing industry is an important way to cultivate a modern industrial system and achieve high-quality development of economy. This study aims to enhance the supporting and leading role of the Greater Bay Area of Guangdong, Hong Kong, and Macao in China's national economic development and opening up by promoting the in-depth integration of these two industries. A new monitoring system is developed to simulate the level of integration of the modern service and advanced manufacturing industries in the Guangdong-Hong Kong-Macao Greater Bay Area. The dynamic comprehensive evaluation model of stock increment was used to simulate the coupling coordination degree of these two industries. Empirical results reveal that the development of these industries has uneven stock and incremental resource advantages, and their development has not been balanced. The coupling coordination degree of these industries in some areas has become maladjusted, while in other areas, it has fluctuated or developed from coordination to disorderly leap-forward. These findings demonstrate that the study methods and results are significant for analyzing industrial integration processes and promoting in-depth integration development in the Guangdong-Hong Kong-Macao Greater Bay Area. In conclusion, building a monitoring system using the dynamic comprehensive evaluation model of stock increment is an effective way to evaluate the level of integration of these two industries and promote their in-depth integration in the Greater Bay Area.